Task Scheduling by Neural Network with Mean Field Annealing Improvement in Grid Computing

Task scheduling is a key concern in developing grid computation application. Desirable goals for grid task scheduling algorithms would shorten average delay and maximize system utilization and fulfil user constraints. In this work, an agent-based grid management infrastructure is coupled with Hopfie...

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Vydané v:2006 Canadian Conference on Electrical and Computer Engineering s. 554 - 557
Hlavní autori: Guixiang Xue, Zheng Zhao, Ma, M., Yantai Shu
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2006
Predmet:
Agents
Annealing
Computer science
Delay
Grid computing
Hopfield neural networks
Intelligent networks
Neural network
Neural networks
Processor scheduling
Resource management
Scheduling algorithm
Task Scheduling
ISSN:0840-7789
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Shrnutí:Task scheduling is a key concern in developing grid computation application. Desirable goals for grid task scheduling algorithms would shorten average delay and maximize system utilization and fulfil user constraints. In this work, an agent-based grid management infrastructure is coupled with Hopfield neural network scheduling algorithm. An agent in a grid utilizes a neural network algorithm to manage and schedule tasks. Hopfield neural network is good at finding optimal solution with multi-constraints and can be fast convergent to the result. The simulation results show that the scheduling algorithm works effectively. Efficient and valid solutions for grid task scheduling can be obtained using the scheme. Hopfield neural network is good at finding optimal solution with multi-constraints and can be fast convergent to the result. However, it is often trapped to a local minimum. Mean field annealing algorithm has an advantage in finding the optimal solution escaping from the local minimum
ISSN:0840-7789
DOI:10.1109/CCECE.2006.277742